Heterogeneous Resources Management in MEC

  • Video Caching and Delivery in MEC-enabled HetNet

By deploying computing and storage nodes in the radio access networks, the computing, caching and communication functionalities integrate and converge at the edge of wireless networks. This provides new opportunities for the quality of service provisioning for the delay-sensitive and computation-intensive mobile services. The joint optimization of cache content placement and user association for mobile video services would be investigated. The mobility of users, the uncertain user’s demand, and the dynamic variability of video file popularity have brought great challenges to our research. ¬†How to provide Video caching and delivery in MEC-enabled HetNet is the subject of our research.

 

  • AI-driven Resources Management Algorithm Design in MEC

These are also our research points that the joint optimization of computing and communication resources based on the tradeoff of quality of computation and service for augment reality services and the QoE-driven joint optimization of computing, caching, and communication for adaptive streaming services. Based on the interaction between the heterogeneous resources utilization and the QoS provisioning, AI-driven resources management algorithm in MEC solves the efficient utilization problems of the heterogeneous resources. The overall objective is to design efficient resources allocation and QoS provisioning algorithms, to improve the utilization efficiency of the heterogeneous resources, and improve the QoS provisioning capability of the wireless networks.

 

  • Computing Offloading and Deployment of MEC

  • High efficient and scalable framework of mobile edge computing in heterogeneous networks. We are devoting to developing the edge computing platform with lightweight and compatible mechanism to adjust to different hardware device and network topology.
  • Cooperative computing offloading for wireless network. Offloading algorithm and mechanism are researched for computing intensive services in order to jointly reduce the delay and energy consumptions for terminals with different QoS levels.
  • Data transmission optimization in distributed networks. Efficient data aggregation and compression schemes are researched for uploading data streams, while the optimization of content caching and distribution is studied for downloading traffic.